\chapter{Conclusion}
\label{sec:conclusion}
Since the release of the Kinect device several PC applictions have been developed. In this project a gesture recognition system for casual user interaction has been implemented, using Canonical correlation analysis to identify gestures from raw image data. The results indicate that CCA can be used to distinguish between different gestures. To achieve more accurate results the tracking of body parts should be improved.


\section{Future work}
There are several ways to improve the obtained result. More advanced tracking methods could be used to make sure that the correct parts of the body are tracked. The proprietary systems for skeleton tracking that are now available could perhaps be used in order to more accurately find the different body parts that are being used to perform a gesture.

A comparison with other algorithms for recognising a sequence of positions, such as Hidden Markov Models, could also be interesting.

